Root Mean Square Error (RMSE) squares the prediction errors, takes the mean, and then takes the square root, making it more sensitive to larger deviations. Its unit is the same as that of the target variable. Lower values are better, and it is one of the most classic error metrics in regression and prediction tasks. It is often reported together with MAE to provide a comprehensive characterization of the error distribution.